Ross A. Industrii buduschego. M.: AST, 2017. 351 s.
Shvab K. Tehnologii chetvertoy promyshlennoy revolyucii. M.: Eksmo, 2018. 320 s.
Shvab K. Chetvertaya promyshlennaya revolyuciya. M.: Eksmo, 2018. 288 s.
Alferev D.A. Iskusstvennyy intellekt v selskom hozyaystve // AgroZooTehnika. 2018. T. 1. № 4. DOI: 10.15838/alt.2018.1.4.5
Shutkov A.A., Anischenko A.N. Buduschee iskusstvennogo intellekta, neyrosetey i cifrovyh tehnologiy v APK // Ekonomika i socium: sovremennye modeli razvitiya. 2019. T. 9. № 4 (26). S. 508–522. DOI: 10.18334/ecsoc.9.4.100454
Adhitya Y., Prakosa W.S., Köppen M. [et al.]. Convolutional Neural Network Application in Smart Farming. International Conference on Soft Computing in Data Science. SCDS 2019: Soft Computing in Data Science, pp. 287–297. URL: https://link.springer.com/chapter/10.1007/978-981-15-0399-3_23
Kamilaris A., Prenafeta-Boldú F.X. A review of the use of convolutional neural networks in agriculture. The Journal of Agricultural Science, 2018, no. 156 (3), pp. 312–322. DOI: 10.1017/S0021859618000436
Obrabotka i analiz cifrovyh izobrazheniy s primerami na LabVIEW IMAQ Vision / Yu.V. Vizilter [i dr.]. M.: DMK Press, 2007. 464 s.
iFarm – sistema raspoznavaniya ryb dlya vyyavleniya bolnyh osobey // TADVISER. 2018. URL: https://www.youtube.com/watch?v=eTtXopobi4U&feature=emb_logo
Dashkovskiy I. Pod kontrolem. Iskusstvennyy intellekt sledit za poryadkom na agropredpriyatiyah // Agroinvestor. 2019. URL: https://www.agroinvestor.ru/technologies/article/31101-pod-kontrolem/
Butrova E.V., Pavlov V.A., Kovkov D.V. Razrabotka rekomendaciy po adaptacii luchshih mirovyh praktik primeneniya rezultatov distancionnogo zondirovaniya zemli dlya resheniya problem v selskom hozyaystve Rossii // Voprosy elektromehaniki. Trudy VNIIEM. 2019. T. 171. № 4. S. 45–52. URL: https://elibrary.ru/item.asp?id=40381452
Sravnitelnyy analiz ispolzovaniya neyrosetevyh algoritmov dlya segmentacii obektov na sputnikovyh snimkah / V. Pavlov [i dr.] // Cifrovaya obrabotka signalov i ee primenenie (DSPA2019): dokl. 21-y mezhdunar. konf. 2019. S. 399–403.
Ronneberger O., Fischer P., Brox T. U-Net: convolutional networks for biomedical imagensegmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015, no. 9351, pp. 234–241.
Badrinarayanan V., Kendall A., Cipolla R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, no. 39 (12), pp. 2481–2495.
Chaurasia A., Culurciello E. LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation. IEEE Visual Communications and Image Processing (VCIP), 2017, pp. 1–4.
Shadrin D., Menshchikov A., Somov A. [et al.]. Enabling Precision Agriculture through Embedded Sensing with Artificial Intelligence. IEEE, 2019. DOI: 10.1109/TIM.2019.2947125
Alferev D.A. Tehnologii II kak metod prognoznoy analitiki // Iskusstvennye obschestva. 2018. № 4. DOI: 10.18254/S0000137-9-1